R workflow for spot analysis

This workflow is designed to take raw Spot output (.csv file) and automatically perform:

√ Import csv file
√ Extraneous row deletion
√ Tripicate averaging
- Background subtraction
- Statistics (one-way ANOVA with post-hoc test)
- Data visualisation

Install Packages

install.packages("dplyr")
install.packages("tidyverse")

Load Libraries

library(dplyr)
library(tidyverse)


Set the Working Directory

setwd("/Volumes/DOOLAN_Research_Team-A13379/IMB team/LAB MEMBERS/JONATHAN TAN/Projects/ANOVA_tutorial/ANOVA_tutorial")

Add any extra directories

dir.create("plots")
dir.create("data")


Import csv

read.csv("Data/P1_spots.csv", header = FALSE) # header = FALSE is included to avoid the error: more columns than column names
P1<-read.csv("Data/P1_spots.csv", header = FALSE) # Create a new variable (P1) and save the contents of the .csv file into that variable


Remove extraneous rows (subsetting)

P1_clean <- slice(P1, -c(1:5)) # Remove (slice) rows 1-5
colnames(P1_clean) <- c("Plate_rows", "Mouse1_r1","Mouse1_r2","Mouse1_r3","Mouse2_r1","Mouse2_r2","Mouse2_r3","Mouse3_r1","Mouse3_r2","Mouse3_r3","Mouse4_r1","Mouse4_r2","Mouse4_r3") # Change to meaningful header names (r1 = replicate 1)
print (P1_clean)
# write.csv(P1_clean, "Data/P1_clean.csv")


Average triplicate wells

P1_average <- pivot_longer(P1_clean, cols = starts_with("M"), names_to = "replicates", values_to = "counts") |> # Concatenate columns (1:12) into one column (replicates)
  mutate(group = gsub(pattern = "\\_r[1|2|3]", replacement = "", x = replicates)) |> # Search headers for xxx_r1, xxx_r2 etc, substitute with ?
  group_by(Plate_rows, group) |> mutate(avg = mean(counts)) |> 
  distinct(Plate_rows,group,avg) |> 
  pivot_wider(id_cols = Plate_rows, names_from = group, values_from = avg) |> as.data.frame()